Human Pose Estimation with Fields of Parts

2014

Conference Paper

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This paper proposes a new formulation of the human pose estimation problem. We present the Fields of Parts model, a binary Conditional Random Field model designed to detect human body parts of articulated people in single images.
The Fields of Parts model is inspired by the idea of Pictorial Structures, it models local appearance and joint spatial configuration of the human body. However the underlying graph structure is entirely different. The idea is simple: we model the presence and absence of a body part at every possible position, orientation, and scale in an image with a binary random variable. This results into a vast number of random variables, however, we show that approximate inference in this model is efficient. Moreover we can encode the very same appearance and spatial structure as in Pictorial Structures models.
This approach allows us to combine ideas from segmentation and pose estimation into a single model. The Fields of Parts model can use evidence from the background, include local color information, and it is connected more densely than a kinematic chain structure. On the challenging Leeds Sports Poses dataset we improve over the Pictorial Structures counterpart by 5.5% in terms of Average Precision of Keypoints (APK).

Author(s):

Kiefel, Martin and Gehler, Peter

Book Title:

Computer Vision – ECCV 2014

Volume:

LNCS 8693

Pages:

331-346

Year:

2014

Month:

September

Series:

Lecture Notes in Computer Science

Editors:

Fleet, David and Pajdla, Tomas and Schiele, Bernt and Tuytelaars, Tinne

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Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems